Municipal solid waste management: Integrated analysis of environmental and economic indicators based on life cycle assessment

Municipal solid waste management: Integrated analysis of environmental and economic indicators based on life cycle assessment

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Journal Pre-proof Municipal solid waste management: Integrated analysis of environmental and economic indicators based on life cycle assessment Michel Xocaira Paes, Gerson Araujo de Medeiros, Sandro Donnnini Mancini, Ana Paula Bortoleto, José Antonio Puppim de Oliveira, Luiz Alexandre Kulay PII:

S0959-6526(19)34718-3

DOI:

https://doi.org/10.1016/j.jclepro.2019.119848

Reference:

JCLP 119848

To appear in:

Journal of Cleaner Production

Received Date: 12 August 2019 Revised Date:

17 December 2019

Accepted Date: 21 December 2019

Please cite this article as: Paes MX, Araujo de Medeiros G, Mancini SD, Bortoleto AP, Puppim de Oliveira JoséAntonio, Kulay LA, Municipal solid waste management: Integrated analysis of environmental and economic indicators based on life cycle assessment, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2019.119848. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Author Contributions Section

All authors (Michel Xocaira Paesa, Gerson Araujo de Medeiros, Sandro Donnnini Mancini, Ana Paula Bortoleto, Jose Antonio Puppim de Oliveira and Luiz Alexandre Kulay) participated in all stages of this work, from conception to this review and final submission. We highlight some important stages: Conceptualization, Methodological Development, Software Choice, Data and Results Analysis, Writing, Drafting and Original Article, Review and Final Edition.

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Municipal Solid Waste Management: Integrated Analysis of Environmental and

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Economic Indicators Based on Life Cycle Assessment

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Michel Xocaira Paesa,,b,*; Gerson Araujo de Medeiros b,*; Sandro Donnnini Mancinib;

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Ana Paula Bortoleto c; José Antonio Puppim de Oliveira a,d,e; Luiz Alexandre Kulay f

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a

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São Paulo, Brazil;

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b

8

c

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Campinas, Brazil;

Fundação Getulio Vargas (FGV), São Paulo School of Management (FGV/EAESP),

Institute of Science and Technology, São Paulo State University, Sorocaba, Brazil

School of Civil Engineering, Architecture and Urban Design, University of Campinas,

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a

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São Paulo, Brazil;

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d

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Administration (FGV/EBAPE), Rio de Janeiro, Brazil;

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e

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Shanghai, China;

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f

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(USP), São Paulo, Brazil.

Fundação Getulio Vargas (FGV), São Paulo School of Management (FGV/EAESP),

Fundação Getulio Vargas (FGV), Brazilian School of Public and Business

School of International Relations and Public Affairs (SIRPA), Fudan University,

Chemical Engineering Department, Polytechnic School, University of São Paulo

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*Corresponding authors: [email protected], [email protected]

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Address: Unesp - Instituto de Ciência e Tecnologia - Câmpus de Sorocaba. Avenida

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Três de Março, 511 - Alto da Boa Vista - Sorocaba/SP - CEP 18087-180

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Tel.: +55 15 3238 3409 1

Abstract

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This paper develops a method to analyse municipal solid waste systems (MSWS) that

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integrates environmental and economic indicators using Life Cycle Assessment (LCA)

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and Life Cycle Costing (LCC). The method was tested in the city of Sorocaba, Brazil, a

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medium size municipality typical of many developing countries. Environmental impacts

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were analyzed considering system expansion, which combined the aspects of primary

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production and recycling processes with the impacts of MSWMS. The economic

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analysis included operating and investment costs to the costs of environmental

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externalities, thus enabling the analysis of total costs to society. An integrated analysis

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of environmental indicators revealed that the most significant reductions in

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environmental impacts occurred in the scenarios with higher rates of reuse of dry waste

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through recycling (70%), which lowered these impacts by up to 50% when compared to

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the current scenario. An analysis of economic performance indicated that the two

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scenarios that combined the highest recycling goals with greater transport efficiency and

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more composting yielded the best results, reducing the total social costs by 31% and

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33%, respectively. Lastly, the integration of environmental and economic analyses

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revealed that the best results are obtained by a combination of composting, mechanical

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biological treatment and recycling, which would reduce the impacts of MSWMS by up

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to 33.7 points per invested dollar. The results supports the application of this proposed

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integrate approach to improve the current solid waste management system in Sorocaba

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and in other cities with a similar system and waste generation.

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Keywords: Environmental Life Cycle Assessment; Life Cycle Costing; Environmental

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and Economic Performance Indicators; Municipal Solid Waste Management; Public

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Policy.

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1

INTRODUCTION

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Significant innovations in waste management have emerged in the last decade to

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address the growing demand for materials and counteract the environmental and social

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impacts of consumption-based economies (Cramer, 2013; Lauridsen and Jørgensen,

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2010; Puppim de Oliveira, 2017, 2019). Programs involving zero waste and the

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diversion of waste from landfills have gained momentum in response to increasing

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urban densification and the growing value of space in the world’s largest cities.

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Moreover, environmental regulations and the indisputable depletion of several material

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resources confirm the benefits of converting end-of-life waste from anthropic processes

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into inputs that can and should be reincorporated either into their own original

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production cycles or into those of other producer or consumer goods (Andrews-Speed et

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al., 2012; EEA, 2014; Paes et al., 2019).

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Cities around the world have made a series of efforts to improve solid waste

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management systems. In some EU countries such as Germany, Austria, Belgium,

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Denmark, the Netherlands and Sweden, the implementation of public policies has raised

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the rates of solid waste reuse, recycling, incineration (with energy recovery) and/or

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composting to 95% (Eurostat, 2019; Word Bank, 2013). What all these cases have in

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common is the adoption of practices for reduction, prevention and non-generation of

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solid waste (Cleary, 2010, 2014; Nessi et al., 2012, 2013). In 2014, the United States

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adopted the landfill alternative for 52% of their volume of solid waste, followed by

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recycling (26%), incineration with energy recovery (13%) and composting (9.0%)

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(EPA, 2018).

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The most recent official estimates, published in 2017, indicated a daily

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generation of 166,000 tons of municipal solid waste (MSW) in Brazil. Out of this total,

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63% was landfilled, about 18% was discarded in open-air dumps without any treatment, 3

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and 5.4% was treated in facilities for sorting, composting and recycling materials in

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order to be recovered. However, no information was obtained from about approximately

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14% of the waste generated (SNIS, 2019). In the state of São Paulo, which has the

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highest gross domestic product (GDP) (US$ 527 billion) and the second-highest per

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capita annual income in the country (US$ 12,075.00) (IBGE, 2019), about 50% of

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MSW was generated in just nine of the 645 municipalities (State of São Paulo, 2015,

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2017). In addition to sharing a similar urbanization profile, all these nine municipalities

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now have more than 500,000 inhabitants. Among them is Sorocaba, the state’s ninth-

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largest, covering an area of 456 km², with a population of 671,000 inhabitants and a

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Human Development Index (HDI) of 0.798 (IBGE, 2019), whose economy is based on

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industry.

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Municipal or local governments in Brazil, similarly to many other countries, are

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responsible for providing and controlling MSW management services. These actions are

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based on legislation, management guidelines, objectives and targets at the local,

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regional and national levels, which generally impose the challenge of rationalizing and

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improving the performance of activities (Guerrero et al., 2013). The set of services,

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infrastructure and operational facilities assigned to the activities of collection, transport,

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sorting, treatment and disposal of solid waste in a municipality is called the Municipal

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Solid Waste Management System (MSWMS) (e.g., Brasil, 2010; SNIS, 2019; World

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Bank, 2013).

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Therefore MSWMS are complex and their effectiveness is not always easy to

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measure, analyze and monitor. From the environmental standpoint, the Life Cycle

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Assessment (LCA) technique has proved to be a suitable tool to evaluate their

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performance, including the effect of actions and scenarios designed for their

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improvement (Lazarevic et al., 2012; Paes et al., 2014, 2018). The scope of application 4

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of LCA and the quantitative nature of its diagnoses enable the introduction of

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comprehensive and accurate assessments in the daily routine of the management and

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decision-making practices associated with these systems (Laurent et al., 2014; UNEP,

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2011).

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The economic performance of MSWMS has also been examined more recently

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based on the concept of Life Cycle Thinking. Massarutto et al. (2011), Petit-Boix et al.

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(2017) and Reich (2005), who used this approach, argue that the application of

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traditional economic methods to systematic scopes, such as those practiced by LCA, can

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offer useful findings for decision-making processes pertaining waste management

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systems. These authors also point out that the inclusion of the costs of the use of natural

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resources and pollution, known as negative environmental externalities, has proved to

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provide important additional information that should be considered in future studies on

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waste management, in addition to the operating and investment costs of these systems.

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They also state that these environmental costs are rarely included and that to ensure

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more accurate analyses of MSW management systems, these aspects should be studied,

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developed, improved, valued and included.

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The purpose of this study, therefore, is to make an integrated evaluation of

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environmental and economic performance indicators of municipal solid waste

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management systems by applying Environmental Life Cycle Assessment (LCA) and

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Life Cycle Costing (LCC) approaches, enabling to structure more complete evaluations

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of MSWMS so they can serve as guidelines to develop new public policies for

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municipal solid waste management.

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Therefore, this study aims to contribute to the advancement of knowledge

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beyond the case study, by developing, building, applying and evaluating an innovative

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method of integration and analysis of environmental and economic performance 5

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indicators, which considers the operating and investments costs of environmental

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externalities, in addition to total social costs, thus filling gaps within LCA and

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management indicators of MSW.

125 126

2 METHODS

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The method involved the following steps: (i) choice of the case and

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characterization of the MSWMS currently operating in the city of Sorocaba (SP), which

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represents a good case to apply the method as the municipality has a set of data that can

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be used in the analytical tools and the authors had access to the city´s data; (ii)

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collection of data and information to underpin the establishment of a representative

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model of the local management of MSW; (iii) diagnosis and evaluation of impacts

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caused by the aforementioned MSWMS; (iv) proposal and specification of analysis

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scenarios; (v) analysis of the environmental performance of these scenarios through the

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identification of environmental impacts and definition of unique environmental

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indicators for each scenario; (vi) examination of the economic performance of each

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scenario based on its operating and investment costs and costs of environmental

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externalities. This stage also involved the development of indicators of total social cost;

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(vii) integration of the environmental and economic indicators pertaining to each

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scenario, in order to make a simultaneous assessment of the influence of these

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dimensions on the performance of the MSWMS, and (viii) proposal of guidelines aimed

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at improving the system and that help support the formulation of public policies.

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2.1 Characterization of the MSWMS of Sorocaba (SP)

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Sorocaba, a municipality in the interior of the state of São Paulo (23° 30' 07” S,

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47° 27' 28” O), covers an area of 456 km², has a population of approximately 670,000, 6

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an industrial economy, and a Human Development Index (HDI) of 0.798 (IBGE, 2019).

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In addition to the region’s economic relevance and the availability of data to conduct the

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study, Sorocaba has solid waste management technologies that are commonly used in

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Brazil, and its average recycling rate of approximately 3.0% is equivalent to that of the

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national standard (PMS, 2014; Paes et al., 2018). These characteristics facilitate the

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replication of research findings to regions with similar profiles.

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Sorocaba’s MSWMS was characterized based on a survey of its MSW (i.e.,

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volume of waste generation and gravimetric composition) and the technologies and

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operational aspects of the system’s operation (Mantovani et al., 2016; Lima and

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Mancini, 2017; Paes, 2018). To this end, recent data were collected along with official

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documents, records of public hearings and meetings held with the drafting committee of

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the Municipal Integrated Waste Management Plan.

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The municipality generated an average of 184,508 tons year-1 of MSW in 2014.

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These materials were collected in the form of ordinary garbage collection and selective

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waste collection (PMS, 2014). Ordinary garbage collection in the municipality is carried

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out from door to door, at different frequencies between the central region (six days

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week-1) and other neighbourhoods (three days week-1). In this case, MSW is collected

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from the generating sources and sent for final disposal in a landfill, without prior

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sorting. The municipality has 25 collector-compactor trucks with a maximum load

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capacity of 15 m³ (or 7.0 t) for ordinary garbage collection, each of which covers an

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average daily distance of 160 km (Paes et al., 2018).

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Sorocaba’s selective waste collection system covered only 15% of the houses in

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the municipality. In this system, the MSW was sent for recycling after being separated

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at sorting centres (PMS, 2014). This selective waste collection was carried out weekly

7

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by worker cooperatives using 12 trucks with 4.0 t load capacity. Each of these trucks

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covered an average daily distance of 36 km (Paes et al., 2018). Sorting was also done by cooperatives that owned the necessary equipment

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(weighing scales, presses, forklifts and waste sorting tables) installed in five sheds.

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The sanitary landfill for Sorocaba’s MSW covers a surface area of 617,000 m²,

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with a capacity to receive 1000 t d-1 of material and an accumulation rate of 9,000,000

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m³ of industrial and domestic waste during its service life (20 years). The landfill,

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located 14 km from the city center, was equipped with liner and had systems for

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collecting leachate and gases generated by the decomposition of organic matter. The

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leachate was collected in basins and then transported to an effluent treatment plant. In

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2014, the landfill gases were released into the air without treatment (PMS, 2014). Table

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1 lists average daily data on the amount of non-recyclable waste, organic and recyclable

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wastes generated in the municipality in 2014.

184 Wastes

Amount (t/day)

Collect

Action

Destination

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CSA

Landfilling

CGA

17

Cooperatives

Separation and Marketing

Recycling

Organic

234

CSA

Landfilling

CGA

Rejects

73

CSA

Landfilling

CGA

Total

505

Recyclable

185 186

Table 1. Characterization and type of waste collection, treatment, use and final disposal of MSW in

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Sorocaba in 2014.

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Source: Adapted from the Municipal Solid Waste Management Plan (PMS, 2014). CSA: Consórcio

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Sorocaba Ambiental (Sorocaba Environmental Consortium), a group of companies that has the

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concession and collects and hauls MSW in the municipality of Sorocaba (SP). CGA: Central de

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Gerenciamento Ambiental (Environmental Management Center) – Sanitary Landfill

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192 193

The data listed in Table 1 describe the three possible types of municipal solid

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wastes (MSW) generated in Sorocaba: (i) Recyclables: plastic, glass, paper and metal

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waste, as well as a variety of packaging, clothing, toys and electronic products sold for

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reuse and recycling; (ii) Organics: including food and garden waste that can potentially

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be reused through biological treatments such as composting and anaerobic digestion;

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and (iii) Rejects: a fraction whose characteristics prevented it from being sold and/or

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recycled in Brazil in 2014 (i.e., metallized films, certain types of glass, rubber, visibly

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contaminated waste paper, thermosets, diapers, animal feces and contaminated toilet

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paper) (PMS, 2014).

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The total waste generated, reused/recovered/recycled and the waste sent to the

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landfill were determined based on the gravimetric characterization of the municipality’s

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MSW (Mantovani et al., 2016; PMS, 2014) and are described in the Supplementary

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Material – Table A. This information served as the basis to develop of a model to

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adequately and coherently represent the system, particularly in terms of resource

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consumption (material and energy) and emissions.

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2.2 Proposed Scenarios

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Another bibliographical survey sought to identify management practices and

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technologies that were not only consolidated and economically accessible but also had

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not yet been applied in Brazil for MSW management. The management and planning

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actions were translated into pre-established goals in the preliminary version of the

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National Solid Waste Management Plan (Brasil, 2011) to reduce the amount of dry and

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wet solid waste destined for landfills.

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Technological actions refer to the practices of composting, mechanical

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biological treatment (MBT) and incineration. These activities were characterized

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considering the experience accumulated by the municipality of Barcelona, Spain, whose

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environmental management structure – especially insofar as public solid waste

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management is concerned – is considered a model among the countries of the European

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Union (EC, 2019). Thus, in addition to official documents obtained from the Barcelona

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City Council (AMB, 2013) and the company Tractament i Selecció de Residus, S.A.

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(Tersa, 2014) and to scientific publications on the subject (Blanco et al., 2016; Colón et

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al., 2012), fieldwork was also carried out to collect data and information describing

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such operations.

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The combination of technological, management and strategic planning actions

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led to eight operating scenarios for Sorocaba’s MSWMS. Scenario S1 corresponded to

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the system operating in the municipality during 2014. The fact that S1 represents the

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system’s actual operating condition means that it serves as the reference for comparison

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of the environmental and economic performance of the other seven scenarios. Scenarios

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S2 to S8 were designed from the composition of the goals established in the National

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Solid Waste Management Plan and the treatment and final disposal alternatives that

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could be incorporated into Sorocaba’s MSWMS. Table 2 summarizes the scenarios as a

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function of the category of solid waste and the treatments employed.

235 Scenario

Type of Waste/ Reject Dry

Wet Form of treatment/ final disposal

S1

Recycling (%) 3.40

Landfill (%) 96.6

Incineration (%) 0.00

Composting (%) 0.00

Landfill (%) 100

MBT (%) 0.00

S2

41.0

0.00

59.0

42.0

58.0

0.00

S3

41.0

0.00

59.0

42.0

0.00

58.0

10

S4

70.0

30.0

0.00

70.0

30.0

0.00

S5

70.0

0.00

30.0

70.0

0.00

30.0

S6*

70.0

0.00

30.0

70.0

0.00

30.0

S7*

70.0

0.00

30.0

70.0

0.00

30.0

S8*

70.0

0.00

30.0

70.0

0.00

30.0

236 237 238 239

Table 2. General characteristics of each scenario concerning wastes and garbage disposal targets and options * The specificities of these scenarios are discussed below throughout the article. MBT: Mechanical Biological Treatment

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The selective waste collection system was used in scenarios S1 to S5 to collect

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the MSW destined for the sorting and composting units. The average fuel consumption

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of ordinary garbage collection was estimated at 2.43 L/t MSW and that of selective

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waste collection at 7.06 L/t MSW, based on the study by Paes et al. (2018). For

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scenarios S6 to S8, it was stipulated that all forms of collection and transportation

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would have the same consumption as that of the conventional garbage collection system

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(2.43 L/t MSW). Table B (Supplementary Material) shows the quantities of transported

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waste and the diesel fuel consumed by the solid waste collection system of each

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scenario.

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In the specific case of S7, the targets remain the same, but all plastic and paper

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waste was destined for incineration. In the case of S8, decentralized composting of 10%

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of the wet wastes were included (to be carried out at the sites of origin, such as gated

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communities, neighbourhoods, schools and public spaces in the city). Consequently, it

253

would forestall the transportation and use of waste treatment units considered in this

254

study for this fraction of wastes. Table C (Supplementary Material) presents the actual

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indices of final disposal, in annual quantities, and reuse of material in each scenario.

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2.3 Life-Cycle Modeling

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The environmental performance was determined based on an attributive LCA

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with a cradle-to-grave approach, according to the ISO 14044 standard (ISO, 2006).

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Thus, the Functional Unit (FU) for the study was defined as “Management of the

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activities of collection, transport, sorting, recycling, treatment and final disposal of

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184,508 t year-1 of MSW generated by the municipality of Sorocaba, Brazil.” Figure 1

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presents the Boundary System of Sorocaba’s MSWMS with its various stages and

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intermediate flows.

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Figure 1. Schematic representation of the Product System comprising Sorocaba’s MSWMS

267 268

The stages of the ordinary garbage collection, selective waste collection, sorting

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and final disposal in a landfill were characterized based on primary data obtained from

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Lima and Mancini (2017), Mantovani et al. (2016), and Paes et al. (2018), as well as on

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fieldwork with the Municipal Government of Sorocaba (PMS, 2014).

12

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Data on energy and water consumption and greenhouse gas (GHG) emissions

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pertaining to the processes of primary production (comprising the steps of extraction of

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raw material and manufacture of products from virgin raw material) and recycling of

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metals, plastics, paper and glass, were garnered from official publications of Brazil’s

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federal government (IPEA, 2010 and EPE, 2014).

277

Consumption and emissions data pertaining to the MSW treatment technologies

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(composting, MBT and incineration) not yet used in Brazil were obtained through

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primary and secondary data, as indicated in section 2.2 – AMB (2013), Blanco et al.

280

(2016), Colón et al. (2012) and TERSA (2014). The MSWMS data from both Sorocaba

281

and Barcelona were based on the guidelines defined by Doka (2009a, 2009b, 2009c) to

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model the diagnoses (S1) and scenarios (S2 to S8).

283

Following the guidelines of Brogaard (2013) and Laurent et al. (2014), the

284

Product System models for the analysis scenarios also considered environmental

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burdens relating to utilities (electricity generation and distribution, treatment of water

286

and liquid effluents), facilities and infrastructure (construction and maintenance of

287

capital goods and roadways). The same applies to the life cycle of diesel oil used by

288

machinery and trucks.

289

Thus, secondary data were used for the environmental aspects about the life

290

cycle of transport activities (such as construction and maintenance of trucks and

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roadways, and petroleum refining for diesel oil production). These data were garnered

292

from the Ecoinvent database, in the form of the following datasets: ‘transport, waste

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collection lorry 21t/ADAPBR U’; Diesel at regional storage/ADAPBR U; Operation

294

maintenance/ADAPBR/I U, road; Road/ADAPBR/I U’ created by Doka GmbH (2009a,

295

2009b, 2009c).

13

296

As for data quality, the geographic coverage consists of the municipality of

297

Sorocaba, and the year 2014 was established as to temporal coverage for this study. The

298

technology coverage comprises two complementary approaches. The first one refers to

299

Sorocaba’s MSWMS, which was specified for this dimension based on the operations,

300

practices, procedures and aspects of infrastructure it comprises. The second approach

301

focuses on technologies such as composting, MBT and incineration, for which the

302

experience and practices of the city of Barcelona were adapted.

303

Lastly, the methodological stage of the Life Cycle Impact Assessment was

304

carried out using the ReCiPe MidPoint V1.13 method (EC, 2010a, Goedkoop, 2009).

305

This stage involved an individual evaluation of the impact categories of Climate Change

306

(CC), Acidification (AC), Eutrophication (EUT), Particulate Matter (PM) and Human

307

Toxicity Potential (HTP), which were then normalized to analyze the contribution of

308

each category of impact from the MSWMS. These categories were chosen because they

309

are frequently used in LCA studies involving solid waste management, as presented by

310

Laurent et al. (2014) and Bernstad and Jansen (2012).

311

Multifunctionality treatment

312

The situations of multifunctionality that occurs in the scenarios described in

313

section 2.2 were treated by cross-border expansion. In this context, following the

314

methodological guidelines proposed in Laurent et al. (2014) and the ILCD Handbook

315

(EC, 2010b), environmental load substitution procedures were used for the recycling of

316

metal, plastic, paper and glass waste and energy recovery in the situations of a sanitary

317

landfill, MBT and incineration.

318

The data considered for recycling processes were water and energy consumption

319

and CO2-eq (carbon dioxide equivalents) emissions. In primary production, which

320

comprises the stages of raw material extraction and product manufacturing from virgin 14

321

raw material, official data on water and energy consumption and CO2-eq emissions

322

published by Brazil’s federal government were also included, as illustrated in Figure 1

323

(IPEA, 2010 and EPE, 2014).

324

The consumption and emissions (from primary production and recycling

325

processes) were calculated concerning waste materials with a potential for reuse but that

326

was discarded in the landfill, in addition to those that were the object of selective waste

327

collection and sorting and sent for recycling.

328

Secondary raw materials were assumed in this study to be able to substitute

329

virgin raw materials reducing environmental impacts without compromising the product

330

quality. Raw materials extraction rates, however, were not expected to reduce or to

331

increase since it also depends on consumption demand for these products.

332

The transport stages about the two processes were not considered in this stage of

333

the study because they were not directly linked to public policies and services but were

334

conducted by private enterprise, making it difficult to obtain accurate estimates in this

335

particular case.

336

Hence, it had to be assumed that the reinsertion of a secondary/recyclable

337

product into the production chain would prevent the environmental impacts resulting

338

from the production of goods from virgin raw material. For instance, by recycling steel

339

or aluminum can, it would no longer be necessary to extract the corresponding amount

340

of iron ore or bauxite to produce a new can from these metals. The same applies to the

341

power generated by waste treatment and disposal units (i.e., Landfill, MBT and

342

Incineration). Thus, in terms of MWh generated, the study considered that the

343

consumption of electricity from the Brazilian energy matrix, which is predominantly

344

hydraulic, would be avoided.

15

345

It should be noted that, despite the cross-border expansion, the environmental

346

burdens (generated and avoided) and the costs of energy generation remained within the

347

MSWMS. Conversely, the environmental burdens (generated and avoided) resulting

348

from primary production and recycling processes were analyzed separately, and their

349

operating and investment costs were not considered since they were also covered by the

350

private sector and not directly by public policies for MSW.

351

The data on energy (MWh) and water (m³) consumption and GHG emissions

352

(tCO2-eq) in each scenario (S1 to S8), described in Table D of the Supplementary

353

Material, were multiplied by the annual total of the materials/wastes sent to final

354

disposal (landfill and incineration, thus requiring new raw material and product) and

355

sorting/recycling units (avoiding primary production processes). Thus, it was possible to

356

make a quantitative estimation of these consumptions and emissions that were

357

effectively incorporated by the actions, and that could be adopted by the MSWMS, by

358

reusing more significant fractions of these dry wastes and recyclable materials.

359 360

2.4 Analysis of Economic Performance

361

The economic analysis of this study was made through the concept of Life Cycle

362

Costing (LCC). This concept can be defined as an evaluation of all the costs associated

363

with a product’s life cycle, which are supported directly by a given stakeholder, with the

364

complementary inclusion of externalities that can be internalized in the future and that

365

are relevant to decision making (Hunkeler et al., 2008; UNEP, 2011).

366

The use of the concept of LCC predates that of the Environmental LCA, and

367

these two methods have been integrated to a certain extent, although the value of LCC

368

for sustainability assessments has been recognized and used (Hunkeler et al., 2008;

369

Iraldo et al., 2017; Swarr et al., 2011). 16

370

Therefore, this study was based on bibliographic reviews and methodological

371

guidelines that emphasize the need to consider not only operating costs but also the

372

costs of externalities identified in a given system, and that could be internalized by the

373

actors, in this case by public policies toward solid wastes. Iraldo et al. (2017), Moreau

374

and Weidema (2015) also point out that further advances are needed in these subjects

375

and that, to this end, studies must be developed aimed at expanding this area of

376

knowledge in order to fill these gaps in the field of LCA and LCC.

377

Based on the scenarios and results of the inventories of environmental LCA, this

378

study used data on operating and investment costs of the MSWMS (PMS, 2014;

379

BNDES, 2014), as well as costs of environmental externalities pertaining to the

380

environmental aspects of energy and water consumption and GHG emissions (IPEA,

381

2010; EEA, 2016). The sum of operating and investment costs with those of

382

environmental externalities made up the total social costs, based on the concepts and

383

definitions of Life Cycle Costing (i.e., Martinez-Sanchez et al., 2016; Petit-Boix et al.,

384

2017).

385

The MSWMS operating costs of 2014 (PMS, 2014) were considered to estimate

386

the costs of ordinary and selective waste collection, sorting and final disposal of MSW

387

in a landfill. The operating and investment costs of composting, MBT and incineration

388

units were estimated based on official data for the year 2014 about the costs of

389

implementing and operating these waste treatment technologies in Brazil (BNDES,

390

2014).

391

The costs of environmental externalities of energy generation in Brazil were

392

estimated based on data from IPEA (2010). The average value defined by these studies,

393

updated for the year 2014, such as environmental costs per MWh of energy, was US$

394

111.17. 17

395

Data published by IPEA (2010) were also used to determine the values of

396

environmental costs of water resources. This study used as reference the valuation

397

methods and the established prices for the use of water by the country’s river basin

398

committees. These prices reached an average of US$ 0.33 per m³ of water, also updated

399

for the year 2014.

400

Lastly, the current market value of the emission permit for one ton of carbon

401

equivalent expressed in t.CO2-eq was used for the GHG. The average price of the

402

equivalent ton of CO2 for the year 2014 was US$ 9.04 per t.CO2-eq (EEA, 2016).

403

The operating and investment costs (O&IC) were calculated based on the values

404

per ton of treated waste – according to the value of each treatment unit (US$.unit) –

405

multiplied by the quantities of waste destined for the units (t.MSW) of each scenario

406

(Sc). The same procedure was adopted for transport, and the costs per ton of transported

407

waste (US$.modalTransport) were multiplied by the amount of waste collected by each

408

mode of transport (t.MSW) in each scenario (Sc). These calculations are illustrated by

409

equation (1) and also described in detail in Table E (Supplementary Material).

410

&

=

$.

.

+

$.

.



1

411 412

The unit costs of environmental externalities (CEE), presented previously, were

413

also multiplied by the water (C-m³) and energy (C-MWh) consumption rates and the

414

annual emissions of GHG (E-CO2-eq.) in each scenario (Sc), according to equation (2):

415

= .

. !"ℎ

.

2%&.

$.

%

'

2

416

18

417

The economic benefits (BE) resulting from the energy (KWh) generated by the

418

waste treatment units in each scenario (Sc) were multiplied by the annual average price

419

of electricity (US$/kWh) in the region under study, determined by means of fieldwork

420

and from bills paid by the municipality, as presented in equation (3). The average

421

annual price paid by the municipality understudy in 2014 was US$ 0.139/MWh.

422

( = )

$. *"ℎ

3

423 424 425

E (Supplementary Material) also describes details of the values and calculations. Lastly, the Total Social Cost (TSC) was calculated using equation (4):

426

: &

– ( +



4

427 428

Quantitative data on water and energy consumption and emissions of CO2-eq. of

429

the externalities of the MSWMS (for each scenario) are also described in Section 3 –

430

Life Cycle Inventory (LCI).

431

For the primary production and recycling externalities, the values listed in Table

432

D – Supplementary Material (Substitution of Environmental Burdens) were multiplied

433

by the quantity of dry wastes sent to the final disposal units (landfill and incineration,

434

which would thus require new raw material and product) and sorting/recycling

435

(avoiding primary production processes), and are described in detail in Table E of the

436

Supplementary Material.

437

The quantities of waste sent to each of the treatment units (which were used to

438

calculate the Operating & Investment Costs) are listed in Table C (Supplementary

439

Material). 19

440

Transport is also explained by the criterion presented under subsection 2.2.

441

According to this criterion, in scenarios S1 to S5, the waste destined for the sorting and

442

composting units was transported via the selective waste collection system (and to the

443

other units, such as landfill, MBT and incineration, via the ordinary garbage collection

444

system). In scenarios S6 to S8, the costs of all forms of collection and transportation

445

were assumed to be the same as that of the ordinary garbage collection system. The

446

results of this procedure and calculations are also listed in Table B of the

447

Supplementary Material.

448

449 450 451

3 RESULTS AND DISCUSSION

3.1 Life Cycle Impact Assessment (LCIA) 3.1.1

Life Cycle Inventory and Characterization Method

452

The Life Cycle Inventory (LCI) data of the MSW treatment and disposal units

453

using the input and output flows for each of the scenarios, based on the functional unit

454

adopted in this study and on the information presented under subsection 2.3, are

455

describes in Table F (Supplementary Material).

456

The quantification and analysis (through the characterization method) of the

457

contributions of each MSWMS activity to the environmental impacts considered in this

458

study – Acidification, Eutrophication, Climate Change, Particulate Matter and Human

459

Toxicity Potential – are presented in a relativized way in Table G (Supplementary

460

Material).

461

20

462

3.1.2

Method of Normalization and Construction of a Single Environmental

463

Performance Indicator

464

Indicators for the Waste Management System

465

The scenario S1 presented a total score of 42,251, while all the other scenarios

466

showed performance improvements. In this case, the total impacts were reduced by 38%

467

in S2, 52% in S3, 34% in S4, 40% in S5, 46% in S6, 49% in S7, and by 52% in S8.

468

In scenario S1, the main contributions in terms of environmental impacts were:

469

climate change (60%), followed by particulate matter (14%), acidification (10%),

470

human toxicity potential (9%) and eutrophication (7%). In S2, contributions to total

471

impacts were ascribed to climate change (39%), acidification (30%), particulate matter

472

(24%), human toxicity potential (5%), and eutrophication (2%).

473

The main contributions to total environmental impacts of scenario S3 would

474

come from acidification (43%), particulate matter (32%), climate change (22%), human

475

toxicity potential (5%), and eutrophication (-2%). This scenario presented impacts

476

avoided by generating electrical energy from waste treatment and disposal units, thereby

477

contributing to reducing the use of hydroelectric sources (which predominate in Brazil’s

478

electricity matrix) and thus reducing the environmental impacts of eutrophication. In

479

scenario S4, which would also dispose of MSW in a landfill, the main contributions to

480

the total environmental impacts would be due to acidification (39%), particulate matter

481

(25%), climate change (24%), human toxicity potential (7%) and eutrophication (4%).

482

In S5, contributions to the total environmental impacts would come from

483

acidification (45%), particulate matter (29%), climate change (17%), human toxicity

484

potential (7%), and eutrophication (4%). In S6, the main contributions to total

485

environmental impacts would be caused by acidification (48%), particulate matter

486

(29%), climate change (16%), human toxicity potential (6%), and eutrophication (2%). 21

487

In S7, the main contributions to total environmental impacts would come from

488

acidification (50%), particulate matter (30%), climate change (16%), and human

489

toxicity potential (5%), while eutrophication would be lower by (-)1. Lastly, the main

490

contributions in S8 would come from acidification (47%), particulate matter (29%),

491

climate change (16), human toxicity potential (6%) and eutrophication (0.1%).

492

Figure A (Supplementary Material) illustrates the overall environmental effects

493

using a single normalized MSWMS indicator, depicting the contributions of each

494

impact category (Acidification, Eutrophication, Climate Change, Particulate Matter and

495

Human Toxicity Potential) for all the scenarios created in this study.

496

This procedure revealed that the environmental performance of the MSWMS

497

could be improved by adopting measures aimed at greater reuse and treatment of

498

municipal solid waste, allied to improvements in the efficiency of the selective waste

499

collection system. Electric power generation and its impacts avoided through MBT and

500

incineration contributed to the good results of scenario S3 (particularly in terms of

501

eutrophication) while composting at the MSW generation sites also contributed to the

502

results of scenario S8.

503

Recycling and Primary Production Indicators

504

This section discusses the potential environmental impacts of CO2-eq. emissions

505

and the water and energy consumption of the processes of primary production and

506

recycling of dry waste, which were defined and considered by the environmental load

507

substitution method and cross-border expansion (see subsection 2.3).

508

Table H in the Supplementary Material analyzes the contributions of the

509

environmental aspects about water and energy consumption and GHG emissions to each

510

impact category considered here (i.e., Acidification, Eutrophication, Climate Change,

511

Particulate Matter and Human Toxicity Potential). And Figure B (Supplementary 22

512

Material) illustrates the total impacts and contributions of each impact category

513

examined in this study, considering recycling and production of virgin raw material.

514

As can be seen in Figure B (Supplementary Material), what stands out in this

515

stage of the study is the reduction of environmental impacts. This is demonstrated, using

516

the single indicator/ normalization method, in scenarios S4, S5, S6 and S8, which have

517

the highest reuse rates of dry recyclable waste (70%), followed by scenarios S2 and S3,

518

in which the reuse rates are lower (41%).

519

Scenario S1 (which was in operation in the year 2014), presented a total score of

520

193,099 and the worst result. Scenarios S2 and S3 showed a reduction of 34% in total

521

impacts. Still in comparison with S1, scenarios S4, S5, S6 and S8 showed a 52%

522

reduction in total impacts. The least significant impact reduction, 18%, was reached in

523

Scenario 7 due to the incineration of plastics and paper, which caused impacts because

524

of the need for primary production of these raw materials.

525

All the analyzed scenarios showed similar contributions from each

526

environmental impact category. The impact categories with the highest contributions to

527

the total impacts were Eutrophication (44% to 45%), followed by Human Toxicity

528

Potential (34%), Climate Change (7% to 8%), Particulate Matter (7%) and Acidification

529

(6%). These impacts were influenced mainly by energy consumption – through Brazil’s

530

hydroelectric matrix – by primary production and recycling activities, followed by CO2-

531

eq. emissions and water consumption, which are listed quantitatively in Table H of the

532

Supplementary Material.

533

Establishment of the Single Indicator

534

The data presented in Figure A (Supplementary Material) - Indicators for the

535

Waste Management System - were then combined with those in Figure B

536

(Supplementary Material) - Recycling and Primary Production Indicators -, enabling the 23

537

development of a single indicator by the normalization method to analyze

538

environmental performance based on cross-border expansion. Figure 2 shows the scores

539

for each scenario, impact reductions and resulting improvements in environmental

540

performance compared to that of the scenario in operation in 2014 (S1).

200,000.00

150,000.00

100,000.00

50,000.00

0.00 S1

S2

S3

Total MSWMS

S4

S5

Total Primary Production and Recycling

S6

S7

S8

Total Product System

541 542

Figure 2: Environmental Performance of the MSWMS, the Primary Production and Recycling Processes

543

and the complete Product System adopted.

544 545

Figure 2 clearly shows that the results were more satisfactory for recycling,

546

based on the sum of the scores of the MSWMS indicators with cross-border expansion

547

for the primary production and recycling activities. This was mainly due to the

548

scenarios that considered greater reuse of dry waste by recycling technologies, such as

549

scenarios S4, S5, S6 and S8, which afforded environmental impact reductions of 49%,

550

50%, 51% and 52%, respectively, when compared to S1. The scenarios involving lower

551

targets for reuse of dry waste, such as S2 and S3, provided lower impact reductions of

24

552

35% and 37%, respectively. The lowest total impact reduction was achieved by scenario

553

S7, i.e., 24%.

554

Also to be noted is the positive contribution of transport in response to the

555

adoption of the efficiency of ordinary garbage collection for the selective waste

556

collection system. Moreover, impacts were avoided through the generation of electricity

557

by waste treatment units and the adoption of preventive measures via composting at the

558

sites of wet waste generation, as described in subsubsection 3.1.2.1 and in Table G and

559

Figure A (Supplementary Material).

560

Averaging the score of the eight scenarios revealed that the impacts of the

561

MSWMS contributed 1/5 (18%) to the total impacts, while 4/5 (82%) would be due to

562

the impacts of cross-border expansion through consumption and the emissions produced

563

by primary production and recycling. In other words, more than merely collecting

564

MSW, society’s profile of consumption and waste disposal, as well as the decision

565

about what to do with discarded waste, have a significant environmental impact.

566 567

3.2 Life Cycle Costing (LCC)

568

Calculations of Financial and Environmental Life Cycle Costing are presented

569

below (Table 3) based on the results of Operating & Investment Costs, Environmental

570

Externalities and Total Social Costs for each of the scenarios.

25

571 572

Table 3: Operating & Investment Costs of the MSWMS (US$/year); Costs of the Environmental Externalities of primary production (PP) plus recycling (R) and of the MSWMS and; Total Social Costs (US$/year) for all the scenarios. Scenarios Ordinary Collection (t)

S1

S2

S3

S4

S5

S6

S7

15,824,391.25

13,142,144.38

13,142,144.38

11,904,703.13

11,904,703.13

Selective Collection (t)

1,997,130.96

11,724,333.51

11,724,333.51

16,212,004.25

16,212,004.25

4,470,337.50

4,470,337.50

4,470,337.50

Subtotal Collection

17,821,52.,21

24,866,477.88

24,866,477.88

28,116,707.38

28,116,707.38

16,375,040.63

16,375,040.63

15,585,763.80

461,05.35

2,706,677.88

2,706,677.88

3,742,700.88

3,742,700.88

3,742,700.88

2,440,349.45

3,742,700.88

Composting (t)

0.00

2,063,162.50

2,063,162.50

3,540,804.17

3,540,804.17

3,540,804.17

3,540,804.17

3,022,030.67

MBT (t)

0.00

0.00

2,931,543.13

0.00

1,485,148.50

1,485,148.50

1,485,148.50

1,485,148.50

Incinerator (t)

0.00

0.00

5,113,041.67

0.00

3,951,430.00

3,951,430.00

5,412,341.67

3,951,430.00

Landfill (t)

7,429,291.67

4,696,333.33

0.00

3,059,916.67

0.00

0.00

0.00

0.00

Subtotal Treatment

7,890,349.02

9,466,173.71

12,814,425.17

10,343,421.71

12,720,083.54

12,720,083.54

12,878,643.79

12,201,310.04

25,711,871.23

34,332,651.60

37,680,903.05

38,460,129.08

40,836,790.92

29,095,124.17

29,253,684.41

27,787,073.84

0.00

1,032,160.14

2,424,889.60

672,508.48

1,416,649.98

1,416,649.98

1,798,404.45

1,416,649.98

25,711,871.23

33,300,491.45

35,256,013.45

37,787,620.60

39,420,140.94

27,678,474.19

27,455,279.96

26,370,423.87

Sorting (t)

Subtotal MSWMS (-) Generated Energy (kWh) Total Costs MSWMS

11,904,703.13

S8

11,904,703.13

11,115,426.30

573

26

574 575 576 577 Scenarios Energy (MWh) Water (m³) CO2eq (t) Subtotal Externalities PP + R Energy (MWh) Water (m³)

S1

S2

S3

S4

S5

S6

S7

S8

47,626,737.77

31,420,795.62

31,420,795.62

23,082,686.21

23,082,686.21

23,082,686.21

39,207,542.86

23,082,686.21

496,212.33

423,434.86

423,434.86

385,930.21

385,930.21

385,930.21

471,814.89

385,930.21

738,952.63

439,889.33

439,889.33

285,763.01

285,763.01

532,307.24

285,763.01

285,763.01

48,861,902.73

32,284,119.81

32,284,119.81

23,754,379.43

23,754,379.43

40,211,664.99

23,754,379.43

23,754,379.43

56,632.75

124,962.45

871,222.06

183,164.87

689,969.26

689,969.26

787,144.49

680,794.67

17,316.20

84,425.68

93,984.70

115,278.35

122,374.44

122,374.44

85,626.10

122,315.15

CO2eq (t)

1,577,897.96

640,925.14

281,946.83

425,277.23

263,486.19

225,144.64

220,976.80

212,258.37

Subtotal Externalities MSWMS

1,651,846.90

850,313.27

1,247,153.60

723,720.46

1,075,829.89

1,037,488.34

1,093,747.39

1,015,368.19

Total Externalities

50,513,749.63

33,134,433.08

33,531,273.40

24,478,099.88

24,830,209.32

24,791,867.77

41,305,412.38

24,769,747.62

Total Costs for Society

76,225,620.86

66,434,924.53

68,787,286.86

62,265,720.48

64,250,350.26

52,470,341.96

68,760,692.34

51,140,171.49

27

578

As can be seen in Table 3, the scenario in operation in 2014 (S1) had a lower

579

annual operating cost of approximately US$ 25.7 million. However, it caused a more

580

significant environmental externality of approximately US$ 50.5 million per year,

581

resulting in an annual social cost of US$ 76.2 million, the highest of all the scenarios.

582

In scenario S2, with composting and recycling (in the range of 40%) plus

583

landfilling, the annual costs and investments increase by 30%, but the externalities

584

decline by approximately 34% compared to S1. Scenario S3 (which has the same

585

recovery targets via composting and recycling as those of S2, plus MBT and

586

incineration of the remainder rather than landfilling), would show an increase of 37% in

587

annual operating costs and a decrease in externalities similar to that of S2, i.e., 34%.

588

Total social costs would decrease by 13% in S2 and by 10% in S3.

589

It is noteworthy that, based on investments in greater reuse of recyclable

590

materials (through the targets of 70% recovery), like in Scenarios 4 and 5, the annual

591

operating costs would increase by 47% and 53%, respectively (compared to S1).

592

However, the externalities would decrease by 52% and 51%, respectively, resulting in a

593

total social cost reduction of 18% in S4 and 16% in S5.

594

When the level of efficiency of ordinary garbage collection is considered for

595

selective waste collection, scenario S6 shows a lower increase in operating and

596

investment costs than the preceding scenarios (S2, S3, S4 and S5), with an increase of

597

8% in comparison to the scenario in operation in 2014 (S1). Scenario S6 would also

598

have a decrease of 51% in the costs of externalities and 31% in total social costs

599

compared to those of S1.

600

Scenario S7, whose characteristics are the same as those of S6, but also

601

incinerates all paper and plastic wastes, shows a 7% increase in operating and

602

investment costs, but a lower reduction in externalities costs (18%) and the same 28

603

reduction in total social costs (10%) as S3. Lastly, S8, a scenario similar to S6, but

604

which adds composting of 10% of organic wastes at the generation sites, shows the

605

lowest increase in operating and investment costs (3%) and the most significant

606

reduction in total social costs (33%). Compared to S1, this scenario also presents a 51%

607

decrease in the costs of environmental externalities.

608

In summary, the results suggest economic gains obtained through the adoption

609

of practices that reduce negative environmental impacts, such as recycling, composting

610

(mainly when decentralized) and MBT, in detriment to incineration and especially

611

landfilling. Based on this curve, the worst scenario is S1, followed by S3, S7, S2, S5,

612

S4, S6 and S8. The latter shows the best economic performance of all the scenarios,

613

with total social costs of US$ 51.1 million compared to US$ 76.2 million of the scenario

614

operating in 2014, i.e., 1/3 lower.

615 616

3.3 Integration of Indicators and Scenarios in LCA to establish Public Policies

617

for MSW Management

618

The inventory and the environmental impact assessment and Life Cycle Costing

619

methods revealed a direct relationship between the improved efficiency of the MSWMS

620

and the increase and better management of costs and investments. Such investments

621

would lead to reductions not only in the environmental impacts of the MSWMS itself

622

and of the affected systems (recycling and primary production), but also in the costs of

623

environmental externalities. Figure 3 illustrates the correlations of the results, providing

624

a more detailed picture of how operational investments are reflected in environmental

625

indicators and externalities costs.

29

(A)

(B)

S1 40,000.00

30,000.00

S4

S2

S5

S6

20,000.00 S8

S3

S7

10,000.00

0.00 20,000.00

626

200,000.00

Environmental Performance - Normalization (Primary Production + Recycling)

Environmental Performance - Normalization (MSWMS)

50,000.00

25,000.00

30,000.00

35,000.00

S1

S7

160,000.00

S3

120,000.00

40,000.00

S2 S6 80,000.00

S4

S5

S8

40,000.00

0.00 20,000.00

25,000.00

30,000.00

35,000.00

40,000.00

Operational and Investments Costs of MSWMS (1,000 dolllars)

Operational and Investments Costs of MSWMS (1,000 dollars)

(C) Environmental Externalities Costs (1,000 dollars)

60,000.00

628 629

S7

40,000.00

S3 30,000.00

S2 S5

S6 S8

20,000.00

S4

10,000.00

0.00 20,000.00

627

S1

50,000.00

25,000.00

30,000.00

35,000.00

40,000.00

Operational and Investments Costs of MSWMS (1,000 dollars)

Figure 3: Correlations between Operating & Investment Costs and Environmental Impacts of the MSWMS (A), Impacts of Primary Production and Recycling (B) and Costs of Environmental Externalities (C).

30

630

Thus, upon adopting the correlation between the reduction in environmental

631

impacts of the MSWMS and the invested monetary value (Figure 3A), there is a

632

reduction of 33.7 points/dollar invested in S8, followed by a reduction of 12

633

points/dollar invested in S7 and 10.1 points/dollar invested in S6. Also note, in Figure

634

3A, that these scenarios (S6, S7 and S8) are closest to the lowest costs (x-axis) and

635

environmental impacts (y-axis). The other scenarios would lead to reductions of 2.1

636

(S2), 2.3 (S3), 1.2 (S4) and 1.3 (S5) points per dollar invested.

637

Based on an evaluation of the impacts of primary production and recycling

638

(Figure 3B), upon also adopting the correlation of impact reduction per invested dollar,

639

one can see that better results would show a reduction of 152 dollar points/dollar

640

invested (S8) and 50.9 points/dollar invested (S6). Scenario S7 would show a reduction

641

of 20.1 points/dollar invested, while the other scenarios show the following

642

relationships and reductions: 8.7 points/dollar invested (S2); 6.9 points/dollar invested

643

(S3); 8.3 points/dollar invested (S4) and 7.3 points/dollar invested (S5).

644

An analysis of the reductions in the costs of environmental externalities per

645

dollar invested in the MSWMS (Figure 3C) indicates that scenario S8 also presents the

646

best result, with a reduction of 39 dollars per dollar invested, followed by S6, with a

647

reduction of 13 dollars per dollar invested, and S7, with a reduction of 5 dollars per

648

dollar invested. The other scenarios show a ratio reduction of 2 dollars per dollar

649

invested.

650

Thus, by means of S7 (Figure 3A), in which primary production and recycling

651

processes were not considered through the method of cross-border expansion and

652

substitution of environmental burdens, heat treatment may reduce the impacts of

653

MSWMS, especially those resulting from atmospheric emissions and the avoided

654

burdens of using electric power, as demonstrated in greater detail in subsubsection 31

655

3.1.2. However, considering the entire product system analyzed here, Figures 3B and

656

3C clearly demonstrate that the indicators show better results, especially in scenarios S6

657

and S8, both of which adopt not only improvements in the selective waste collection

658

system but also zero landfilling and the highest recycling and composting rates

659

considered in this study (70%). Of these two scenarios, S8 is even better, because it

660

differs in that part of the composting takes place at the waste generation sites, thereby

661

reducing transport-related impacts.

662

The above-described procedure revealed the importance of integrating the

663

analysis of operating and investment costs with environmental indicators and

664

externalities costs. The use of the cross-border expansion method and the substitution of

665

environmental burdens from primary production through recycling enabled us to

666

determine how both the environmental performance and the costs of environmental

667

externalities of the MSWMS are related to its efficiency and investment spending.

668

Based on the results presented herein, the Life Cycle Impact Assessment and

669

Life Cycle Costing techniques proved to be suitable for the analysis of scenarios and

670

can, therefore, be used as management and decision making tools for public policies on

671

MSW management (i.e. Silva et al., 2017; Xu et al., 2018). These techniques enabled

672

the proposal of management guidelines and actions aligned with national laws and

673

international best practices and trends (i.e. Brockmann et al., 2018; Restrepo and

674

Morales-Pinzón, 2018; Santos et al., 2018). These best practices and trends include the

675

following:

676

Promote the expansion and improvement of Selective Waste Collection, Sorting

677

and Recycling systems by increasing the efficiency of selective waste collection

678

transport systems and implementing actions aimed at gradually, albeit rapidly,

679

increasing and universalize selective waste collection, sorting and recycling services; 32

680

Establish progressive incentives to those that implement local alternatives for

681

waste management and treatment, such as reduction and/or exemption of the “solid

682

waste fee” for gated communities, subdivisions, neighbourhoods and residences that

683

adopt not only the selective waste collection but also onsite composting. Another

684

alternative would be to implement a system known worldwide as PAYT, an acronym

685

for Pay As You Throw (i.e., EPA, 2006), which could encourage sites that adopt waste

686

reduction and prevention measures;

687

Promote the Reuse and Treatment of MSW, based on the economic instruments

688

set forth in the National and Municipal Climate Change Policy (Brasil, 2009; PMS,

689

2016), This could be done, for example, through tax, fiscal, financial and economic

690

incentives for management systems and technologies that produce lower GHG

691

emissions and for carbon credit projects and the Clean Development Mechanism

692

(CDM). However, note that this item and action should be a topic and subject of

693

discussion by municipalities at the national and state levels of government, through the

694

establishment of broader laws to incentivize technological and management alternatives

695

that contribute to expanding the reuse of solid waste;

696

Implement decentralized actions in MSW management, aimed at broadening

697

participation of the population through local alternatives that contribute to reducing the

698

amount of MSW sent to collection, transportation and waste disposal systems, such as

699

landfills;

700

Reduce the environmental impacts of waste disposal systems predominantly

701

used in Brazil (Sanitary Landfills), by burning the methane (CH4) emanating from

702

landfills and using it to generate energy.

703

Thus, this study is expected to be useful for similar local and MSWMS

704

managers in terms of actions aimed at improving their systems and in the developing of 33

705

public policies that deal with the (complex) issue of MSW. Discussions to improve

706

MSWMS have converged in the identification of the potential of management

707

technologies and practices aimed at reducing the environmental and economic impacts

708

of waste management.

709

This study has limitations in its analytical approach as the model only integrates

710

environmental and economic performance indicators. Contextual and social aspects,

711

such as consumption rates or health issues, were not considered since more detail

712

analytical approach for social life cycle assessment are still in progress (e.g., Jørgensen

713

et al., 2008). Future studies should consider to apply this method for solid waste

714

management policies.

715 716

4

CONCLUSIONS

717

The integration of economic and environmental indicators through the Life

718

Cycle Thinking provides coherent diagnostics of complex and broad systems. In the

719

same way, this approach is effective in establishing scenarios that can underpin

720

adequate long-term planning of public policies in the MSW domain.

721

By considering other processes, procedures as system expansion and the

722

substitution of environmental loads, adopted to solving multifunctional situations,

723

proved to be fundamental for understanding of activities that are (or may be) affected by

724

decisions relevant to MSW management.

725

The MSWMS option that maximized recycling of dry waste and composting of

726

wet waste, which was partially carried out on-site, presented the best ratio between

727

environmental impacts and total social costs. Conversely, when one considers only

728

operating and investment costs, the scenario composed by landfilling (96.6% of waste

729

disposal) and recycling (3.4%), is the cheapest alternative, which is why it is frequently 34

730

adopted in most of developing countries, including Brazil. However, the economic

731

benefits achieved with less impacting scenarios compensate the higher investment and

732

operating costs required by them, providing advantages even for economies that are still

733

in the process of establishment.

734

As mentioned before, this study can subsidize MSWMS public managers from

735

similar municipalities as Sorocaba in terms of both establish actions aimed at improving

736

their systems and in the developing of public policies that deal with such (complex)

737

issue. Moreover, limitations as absence of some important social aspects – e.g.

738

consumption rates or health issues –, should be surpassed in future studies in order that

739

diagnosis like this could even more fully and consistently support the formulation of

740

public policies on the topic.

741 742

ACKNOWLEDGEMENTS

743

The autors would like to express gratitude to CAPES (Coordenação de

744

Aperfeiçoamento de Pessoal de Nível Superior) for the doctoral fellowship and

745

FAPESP (São Paulo Research Foundation) for the postdoctoral fellowship awarded to

746

Dr. Paes (Grant #2018/16542-0).

747 748

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47

HIGHLIGHTS •

A tool for solid waste management performance analysis based on LCA is presented



Operating costs, externalities and total social cost were obtained and analyzed



Economic and environmental issues of each option were examined in an integrated way



The best option combines composting, mechanical biological treatment and recycling



Impact decrease was up to 34 points/US$ invested compared to the current situation

List of abbreviations Acidification (AC) Clean Development Mechanism (CDM) Climate Change (CC) Environmental Life Cycle Assessment (LCA) Eutrophication (EUT) Functional Unit (FU) Greenhouse Gas (GHG) Gross Domestic Product (GDP) Human Development Index (HDI) Human Toxicity Potential (HTP) Life Cycle Costing (LCC) Mechanical Biological Treatment (MBT) Municipal Solid Waste (MSW) Municipal Solid Waste Management Systems (MSWMS) Particulate Matter (PM) Pay As You Throw (PAYT)

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: